Speech recognition has simplified many tasks in the workplace by permitting a hands-free exchange of information. A worker may receive voice commands through a headset speaker and transmit spoken responses via a headset microphone. The headset may be attached to a mobile computing device, the combination forming a wireless, wearable terminal. Industries, such as inventory management, especially benefit from the hands-free and wireless aspects of these devices.
The inventory-management industry relies on computerized inventory-management systems to aid with various functions. An inventory-management system typically includes a central computer in communication with the wireless, wearable terminals. Workers, wearing the wireless wearable terminals interface with the central computer while performing various tasks (e.g., order filling, stocking, and picking). For example, as a worker is assigned a task, appropriate information is translated into voice instructions and is transmitted to the worker via a headset. As the worker completes the task, the worker may respond into the headset's microphone. In this way, the workers may pose questions and/or report progress and working conditions (e.g., inventory shortages). The wireless, wearable terminal using speech recognition, allows a worker to perform tasks virtually hands-free, improving speed, accuracy, and efficiency.
In an exemplary workflow, the central computer may send voice messages welcoming the worker to the inventory management system and then assigning the worker a particular task (e.g., loading a truck). The system then vocally directs the worker to a particular aisle and bin, and directs the worker to pick a quantity of an item. Upon completing the pick task, the worker vocally confirms the location and the number of picked items. The system may then direct the worker to load the items onto a truck at a particular loading dock. Again, the user responds with feedback at various times during the process. The communications exchanged between the wireless-wearable terminal and the central computer can be task-specific and highly variable.
Good speech recognition is necessary for this work to be performed efficiently. A speech recognizer uses algorithms running on an integrated processor to analyze received speech input and determine the likely word, or words, that were spoken (i.e., form a hypothesis). As part of the hypothesis formulation, the speech recognizer assigns confidence scores that quantitatively indicate how confident the recognizer is that its hypothesis is correct. If the confidence score is above an acceptance threshold, then the speech recognizer accepts the hypothesis as recognized speech. If, however, the confidence score is below the acceptance threshold, then the speech recognizer considers the speech not recognized (e.g., background noise). This rejection may require the user to repeat the speech input. If the acceptance threshold is too high, then correct speech with a low confidence score may be rejected unnecessarily. These unnecessary rejections may reduce productivity and efficiency.
A speech recognizer that utilizes an expected response to adjust the acceptance threshold has been disclosed (e.g., U.S. Pat. No. 7,865,362). Here, however, the expected response is limited to expected responses known in their entirety and does not support specifying the partial knowledge of an expected response. Therefore, a need exists for a speech recognizer that accepts a more generalized expected response for modifying the behavior of the speech recognition system to improve recognition accuracy.